library(tidyverse) # for graphing and data cleaning
library(gardenR) # for Lisa's garden data
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(geofacet) # for special faceting with US map layout
theme_set(theme_minimal()) # My favorite ggplot() theme :)
# Lisa's garden data
data("garden_harvest")
# Seeds/plants (and other garden supply) costs
data("garden_spending")
# Planting dates and locations
data("garden_planting")
# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.garden_harvest %>%
mutate(day = wday(date, label = TRUE)) %>%
group_by(vegetable, day) %>%
summarise(total_harvest = sum(weight) * 0.0022) %>%
pivot_wider(names_from = day, values_from = total_harvest)
garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?There are some rows that do not have any data in garden_planting and so we would want to use a joining function that does not keep every row in garden_harvest.
garden_harvest %>%
group_by(variety) %>%
summarise(total_harvest = sum(weight) * 0.0022) %>%
left_join(garden_planting, by = "variety")
I would like to understand how much money I “saved” by gardening, for each vegetable type. Describe how I could use the garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful. I would start by joining garden_harvest and garden_spending using left_join by variety. Then I would add the data from whole foods. I would mutate a new variable that subtracts the price from the whole foods. I could then arrange in descending order to see which vegetables I saved the most money for harvesting.
Subset the data to tomatoes. Reorder the tomato varieties from smallest to largest first harvest date. Create a barplot of total harvest in pounds for each variety, in the new order.
garden_harvest %>%
filter(vegetable == "tomatoes") %>%
arrange(date) %>%
group_by(variety) %>%
mutate(total_harvest = sum(weight) * 0.0022) %>%
ggplot() +
geom_col(aes(y = variety, x = total_harvest)) +
labs(title = "Total Harvest (lbs) of Varieties of Tomatoes",
x = "",
y = "")
garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().garden_harvest %>%
group_by(vegetable) %>%
summarise(variety_lower = str_to_lower(variety),
variety_length = str_length(variety)) %>%
arrange(variety_length)
garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().garden_harvest %>%
mutate(distinct_variety = str_detect(variety,"er|ar")) %>%
filter(distinct_variety == "TRUE")
In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.
{300px}
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Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data-Small.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.
It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:
sdate. Use geom_density().Trips %>%
ggplot() +
geom_density(aes(x= sdate)) +
labs( title = "Frequency of Bike Rentals",
x = "",
y = "") +
theme(axis.text.y = element_blank())
mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = hour + minute/60) %>%
ggplot() +
geom_density(aes(x = time)) +
labs(title = "Average Frequency of Bike Rentals in 24 Hours",
x = "",
y = "")
Trips %>%
mutate(day = wday(sdate, label = TRUE)) %>%
ggplot() +
geom_bar(aes(y = day)) +
labs(title = "Frequecny of Bike Rentals over Days of Week",
x = "",
y = "")
Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = hour + minute/60) %>%
mutate(day = wday(sdate, label = TRUE)) %>%
ggplot() +
geom_density(aes(x = time)) +
facet_wrap(vars(day), scales = "free") +
labs(title = "Frequecny of Bike Rentals over Days of Week",
x = "",
y = "")
The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.
fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = hour + minute/60) %>%
mutate(day = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time, fill = client)) +
geom_density(alpha = .5, color = NA) +
facet_wrap(vars(day), scales = "free") +
labs(title = "Frequecny of Bike Rentals over Days of Week",
x = "",
y = "")
position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each? The data in this graph is easier to interpret and overall nicer to look at but it can be a bit discheving skewing the data to look as if the registered levels are much lower than the client levels.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = hour + minute/60) %>%
mutate(day = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time, fill = client)) +
geom_density(alpha = .5, color = NA, position = position_stack()) +
facet_wrap(vars(day), scales = "free") +
labs(title = "Frequecny of Bike Rentals over Days of Week",
x = "",
y = "")
position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = hour + minute/60,
day = wday(sdate, label = TRUE),
weekend = ifelse(day %in% c("Sat","Sun"), "weekend", "weekday")) %>%
ggplot(aes(x = time, fill = client)) +
geom_density(alpha = .5, color = NA) +
facet_wrap(vars(weekend), scales = "free") +
labs(title = "Frequecny of Bike Rentals on Weekends and Weekdays",
x = "",
y = "")
client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other? This graph shows each day’s data rather the average for the weekend and weekday. Considering the short attention most people give to graphs I do not think this graph is better because it takes more time to interpet and is more complex.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = hour + minute/60,
day = wday(sdate, label = TRUE),
weekend = ifelse(day %in% c("Sat","Sun"), "weekend", "weekday")) %>%
ggplot(aes(x = time, fill = day)) +
geom_density(alpha = .5, color = NA) +
facet_wrap(vars(client), scales = "free") +
labs(title = "Frequecny of Bike Rentals over a 24 Hour Period",
x = "",
y = "")
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!Trips %>%
left_join(Stations, by = c( "sstation" = "name")) %>%
group_by(lat,long) %>%
summarise(departures = n()) %>%
ggplot() +
geom_point(aes(y=lat, x = long, color = departures)) +
labs(title = "A Map of the Total Number of Departures",
x = "",
y = "")
The stations with a higher proportion of casual users are on the outside of the center as well as clustered in the center.
Trips %>%
left_join(Stations, by = c( "sstation" = "name")) %>%
group_by(lat, long) %>%
summarise(prop_departures = sum(client == "Casual")/n()) %>%
ggplot() +
geom_point(aes(y = lat, x = long, color = prop_departures)) +
labs(title = "Map of the Stations' Casual vs Registered Proportions",
x = "",
y = "")
as_date(sdate) converts sdate from date-time format to date format.Top_Ten_Stations <-
Trips %>%
mutate(just_date = as_date(sdate)) %>%
group_by(sstation, just_date) %>%
summarise(departures = n()) %>%
arrange(desc(departures)) %>%
head(n = 10)
Top_Ten_Stations
Trips %>%
mutate(just_date = as_date(sdate)) %>%
right_join(Top_Ten_Stations, by = c("sstation", "just_date"))
Trips %>%
mutate(just_date = as_date(sdate)) %>%
right_join(Top_Ten_Stations, by = c("sstation", "just_date")) %>%
mutate(day = wday(sdate, label = TRUE)) %>%
group_by(client,day)
summarise(proportion = n())
## Error: `n()` must only be used inside dplyr verbs.
DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.
This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.
facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?